The temporal input being investigated comes in the form of the biologically-inspired DVS camera ([1]}). The DVS sensor sends data in packets, called "events", rather than frames, and is a good candidate to take advantage of the independent nature of each neuron's input in an SNN. The DVS camera is inspired by certain retinal ganglion cells that spike when a change in intensity is detected ([2]}). Similarly, rather than send all pixels at the same time, as with RGB cameras, the DVS camera sends only events that correspond to large changes in pixel intensity. Because of the current instruction-based hardware, this network batches the events into discrete frames, although it could be transplanted into a neuromorphic chip hardware in the future that receives input asynchronously. Recent work has been devoted to discovering good encoding mechanisms for DVS data for use in rate encoding network, whereas we make the argument that the raw DVS events themselves work well with the SNN network structure.
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